Yes but how would that work? If you'd just include people with MS, or Ank Spond in your ME/CFS cohort you might artificially just get more significant findings but your diagnostic criteria to get there will mean that you're not actually doing anything meaningful anymore. Maybe a certain Fukuda cohort ends up having many hits due to MS etc.In ME/CFS we have the problem that we don't know what diagnostic criteria are best.
We can create different diagnostic criteria according to what we believe best describes the disease, but that's just an opinion-based process.
If we had an objective method for defining ME/CFS, then we could develop and refine diagnostic criteria until they could capture the disease with high accuracy.
I'm wondering if a GWAS can be used to provide some much needed objectivity and help validate or refine the diagnostic criteria, or discover subtypes.
DecodeME showed that you can just ask people whether they have ME/CFS (edit: in the sense as mentioned by Andi below) and actually get some genes from that. But it isn't clear whether that is applicable to other cohorts and to what extent (the failure to replicate suggests it won't work for badly selected cohorts but we don't know what the results will look like in clinical cohorts or say a "German DecodeME cohort"). I think a GWAS in a country including a clincal cohort and a DecodeME like cohort could be a useful next step to understand how the signals behave then.
I think it should be fairly simply to do a grading how symptoms correlated to significant gene presence, but I don't think that's necessarily meaningful for identifying diagnostic criteria.
The problem here would be the same as always: Against what value do you define high accuracy of disease? Every criteria is 100% accurate against itself and we don't know any diseases processes as of yet.
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